Enhancing Efficiency of Grid-Connected Solar Photovoltaic System with Particle Swarm Optimization & Long Short-Term Memory Hybrid Technique

Ramakanta Jena, Ritesh Dash, Kalvakurthi Jyotheeswara Reddy, Prasanta Kumar Parida, Chittathuru Dhanamjayulu (), Sarat Chandra Swain and S. M. Muyeen ()
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Ramakanta Jena: Department of Electrical Engineering, Seemanta Engineering College, Mayurbhanj 757086, India
Ritesh Dash: School of Electrical and Electronics Engineering, REVA University, Bengaluru 560064, India
Kalvakurthi Jyotheeswara Reddy: School of Electrical and Electronics Engineering, REVA University, Bengaluru 560064, India
Prasanta Kumar Parida: School of Rural Management, KIIT Deemed to be University, Patia, Bhubaneswar 751024, India
Chittathuru Dhanamjayulu: School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India
Sarat Chandra Swain: School of Electrical Engineering, KIIT Deemed to be University, Patia, Bhubaneswar 751024, India
S. M. Muyeen: Department of Electrical Engineering, Qatar University, Doha 2713, Qatar

Sustainability, 2023, vol. 15, issue 11, 1-24

Abstract: Maximum Power Point Tracking (MPPT) is a technique used in photovoltaic (PV) systems to maximize the power output from the solar panel by constantly tracking and adjusting the optimal operating point. To achieve this, various algorithms have been developed, with Particle Swarm Optimization (PSO) being a widely used method. By adjusting the control system’s parameters, PSO can determine the optimal operating point of the solar panel and improve its overall performance. PSO employs swarm intelligence by simulating the behavior of particles to find the best solution for a given problem. Long Short-Term Memory (LSTM) belongs to the family of Recurrent Neural Networks (RNN) in machine learning and is designed to address the limitations of traditional RNNs in capturing long-term dependencies that exist in sequential data. The combination of PSO and LSTM techniques can result in an efficient MPPT algorithm that leverages the benefits of both. PSO is utilized to optimize the control parameters of the MPPT algorithm, while LSTM is used to predict the solar panel’s power output based on historical data. Consequently, this integration can lead to an accurate and efficient MPPT algorithm that can effectively track the solar panel’s maximum power point. In this research article, an effort has been made to control the duty cycle of the converter by suitably controlling the system gain. A Matlab-based Simulink model in conjunction with Python programming has been used to make the system more robust.

Keywords: algorithm; GA; PSO; PSO-LSTM; search space (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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